Contour Model-Based Hand-Gesture Recognition Using the Kinect Sensor

Abstract

In RGB-D sensor-based pose estimation, training data collection is often a challenging task. In this paper, we propose a new hand motion capture procedure for establishing the real gesture data set. A 14-patch hand partition scheme is designed for color-based semiautomatic labeling. This method is integrated into a vision-based hand gesture recognition framework for developing desktop applications. We use the Kinect sensor to achieve more reliable and accurate tracking under unconstrained conditions. Moreover, a hand contour model is proposed to simplify the gesture matching process, which can reduce the computational complexity of gesture matching. This framework allows tracking hand gestures in 3-D space and matching gestures with simple contour model, and thus supports complex real-time interactions. The experimental evaluations and a real-world demo of hand gesture interaction demonstrate the effectiveness of this framework.